Improvement of image quality in multispectral PET by energy space smoothing and detector space normalization
Bibliographic record
Abstract
By allowing independent data processing in each energy frame, multispectral PET has the potential to improve sensitivity and to support more accurate energy dependent scatter correction in high resolution PET. However, statistical fluctuations associated with the use of multiple energy windows and short acquisition times seriously undermine this potential. In this work, the authors show that this limitation can be overcome without resolution loss, a) by filtering data in the energy space to suppress statistical fluctuations and b) by multispectral normalization of detector efficiency in the spatial domain to eliminate systematic fluctuations. The effectiveness of these corrections was investigated by comparing images acquired in different energy frames with and without energy space filtering. The sharpness and contrast in different energy frames improved significantly. The standard deviation decreased in both the hot regions and background. The FWHM and FWTM evaluated from the images of a line source confirmed that smoothing in the energy space does not degrade image resolution. The work demonstrates that smoothing in the energy space in conjunction with multispectral normalization in the detector space provides adequate data for further processing such as energy-dependent scatter correction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".